Systems and Methods for Generating Flight Plans Used by a Ride Sharing Network

ABSTRACT

The present disclosure provides systems and methods for systems and methods for generating potential flight plans to be used by a ride sharing network, including dynamic and/or automated changes to flight plans that have been engaged with passengers based on real-time information. In particular, the systems and methods of the present disclosure can operate to generate a fleet-level set of potential flight plans which comply with one or more constraints for a fleet of aircraft. The potential flight plans can be exposed into and used by a ride sharing network to provide transportation to users.

RELATED APPLICATIONS

This application claims priority to and the benefit of U.S. ProvisionalPatent Application No. 62/994,320, filed Mar. 25, 2020, which is herebyincorporated by reference in its entirety.

FIELD

The present disclosure relates generally to facilitating ride sharing ofaircraft flights. More particularly, the present disclosure relates tosystems and methods for planning flights to be used by a ride sharingnetwork, including dynamic changes to flight plans based on real-timeinformation.

BACKGROUND

Transportation services exist which enable individual users to requesttransportation on demand. For example, transportation services currentlyexist which enable drivers of ground-based vehicles (e.g., “cars”) toprovide transportation services for potential passengers, as well as todeliver packages, goods, and/or prepared foods.

However, as urban areas become increasingly dense, ground infrastructuresuch as roadways will become increasingly constrained and congested and,as a result, ground-based transportation may not suitably serve thetransportation needs of a significant number of users.

SUMMARY

Aspects and advantages of embodiments of the present disclosure will beset forth in part in the following description, or can be learned fromthe description, or can be learned through practice of the embodiments.

One example aspect of the present disclosure is directed to a computingsystem configured to generate flight plans for a ride sharing network.The computing system includes one or more processors and one or morenon-transitory computer-readable media that collectively storeinstructions that, when executed by the one or more processors, causethe computing system to perform operations. The operations includereceiving input data descriptive of a fleet of aircraft, one or moreconstraints, and a flight planning period. The operations includegenerating a set of potential flight plans for the fleet of aircraft andthe flight planning period based at least in part on the input data. Theoperations include exposing the set of potential flight plans to theride sharing network. The operations include receiving, from the ridesharing network, one or more additions of one or more passengers to oneor more of the potential flight plans to generate one or more engagedflight plans.

Other aspects of the present disclosure are directed to various systems,apparatuses, non-transitory computer-readable media, user interfaces,and electronic devices.

These and other features, aspects, and advantages of various embodimentsof the present disclosure will become better understood with referenceto the following description and appended claims. The accompanyingdrawings, which are incorporated in and constitute a part of thisspecification, illustrate example embodiments of the present disclosureand, together with the description, serve to explain the relatedprinciples.

BRIEF DESCRIPTION OF THE DRAWINGS

Detailed discussion of embodiments directed to one of ordinary skill inthe art is set forth in the specification, which makes reference to theappended figures, in which:

FIG. 1 depicts a block diagram of an example computing system accordingto example embodiments of the present disclosure.

FIG. 2 depicts a graphical diagram of an example set of flight pathsbetween an example set of transportation nodes according to exampleembodiments of the present disclosure.

FIG. 3 depicts a graphical diagram of an example transportation nodeaccording to example embodiments of the present disclosure.

FIG. 4 depicts a flow chart diagram of an example method to generate anduse a set of flight plans within a ride sharing network according toexample embodiments of the present disclosure.

FIG. 5 depicts a flow chart diagram of an example method to generate aset of potential flight plans on an aircraft-by-aircraft basis accordingto example embodiments of the present disclosure.

FIG. 6 depicts a flow chart diagram of an example method to generate aset of potential flight plans based on priority tiers according toexample embodiments of the present disclosure.

DETAILED DESCRIPTION

Example aspects of the present disclosure are directed to systems andmethods for generating potential flight plans to be used by a ridesharing network, including, in some implementations, dynamic and/orautomated changes to flight plans based on real-time information. Inparticular, the systems and methods of the present disclosure canoperate to generate a fleet-level set of potential flight plans whichcomply with one or more constraints for a fleet of aircraft. Thepotential flight plans can be introduced into and used by a ride sharingnetwork to provide transportation service to users. For example, theride sharing network can add passengers to a potential flight plan toengage the flight plan (e.g., bring the flight plan into operation). Theflights associated with engaged flight plans may be provided as astand-alone transportation service or may be a portion of larger,multi-leg transportation service itinerary which utilizes multiplemodalities of transportation such as a mix of transportation via car andtransportation via aircraft. In addition, the systems and methods of thepresent disclosure provide manual and/or automatic tools for flight planadjustment to, for example, handle and mitigate delays or otherreal-time impacts that cause deviation of the fleet of aircraft from theset of flight plans.

More particularly, a flight planning system implemented by one or morecomputing devices can receive a set of input data that describes a fleetof aircraft, one or more flight planning constraints, and a flightplanning period. The input data can be provided by a user and/or canautomatically ingested (e.g., periodically such as daily). For example,in some implementations, disparate aircraft owners/operators caninteract with a computing system (e.g., application implemented thereby)to provide information on the availability of their aircraft forparticipation in the ride sharing network. The provided information canthen be accessed by the flight planning system. In otherimplementations, information about the fleet of available aircraft comesfrom a single centralized source that oversees all aircraft operations.

The fleet of aircraft can include any number of aircraft of the same ordifferent models owned and/or operated by one or more different aerialoperators. Example aircraft that can be included in the fleet includehelicopters or other vertical take-off and landing aircraft (VTOL) suchas electric vertical take-off and landing aircraft (eVTOL). In someimplementations, the fleet of aircraft can include aircraft that arecapable of varying levels of autonomous flight/motion, including,non-autonomous aircraft, semi-autonomous aircraft, and full-autonomousaircraft. Each aircraft can be owned, maintained, and/or operated by oneor more different aerial vehicle operators. By way of example, an aerialvehicle operator can include any entity with operational control (e.g.,ownership, license, etc.) over one or more aerial vehicles. Each aerialvehicle operator can make the one or more aerial vehicles availableduring the flight planning period.

The flight planning period can define an overall start and end date andtime for which the flight planning system should generate potentialflight plans for the fleet of aircraft. Example flight planning periodsinclude spans of 3 hours, 24 hours, a week, etc. The flight planningperiod can be continuous or include disjointed periods of time.

The constraints described by the set of input data can include anynumber of different constraints associated with each aircraft such as,as examples: a respective starting location for each aircraft; arespective ending location for each aircraft; respective times at whicheach aircraft is available or unavailable; respective starting fuel orcharge levels for each aircraft; respective capabilities or attributesof each aircraft such as number of passenger seats available, maximumconsecutive operational time, maximum or minimum flight range, maximumor minimum flight altitude, noise levels associated with operation ofthe aircraft, etc.

The constraints described by the set of input data can also describe afixed infrastructure that the fleet of aircraft must utilize. Moreparticularly, in some implementations, the aircraft can operateaccording to or within a fixed infrastructure in which the ability ofpassengers to embark and disembark the aircraft is constrained to adefined set of transportation nodes. As one example, aircraft can beconstrained to load and unload passengers only at a defined set ofphysical take-off and/or landing areas which may, in some instances, bereferred to as skyports. To provide an example, a large urban area mayhave dozens of transportation nodes located at various locations withinthe urban area. Each transportation node can include one or moretake-off and/or landing pads and/or other infrastructure to enablepassengers to safely embark or disembark from aircraft. Transportationnodes can also include charging equipment, refueling equipment, and/orother infrastructure for enabling aircraft operation. The take-offand/or landing pads of the transportation nodes can be located atground-level and/or elevated from ground-level (e.g., atop a building).Thus, the input data to the flight planning system can also providevarious constraints associated with each respective transportation nodesuch as, as examples: location; the type of aircraft that are able totake-off/land at the transportation node; the number of take-off and/orlanding pads at the transportation node; the number of fueling orcharging structures at the transportation node; the minimum turn-aroundtime for an aircraft to land and then take-off from a transportationnode; how frequently aircraft can arrive and depart the transportationnode (e.g., throughput); airspace approach requirements; and/or otherdescriptors of the different transportation nodes.

In some implementations, the constraints described by the set of inputdata can also describe an availability of other resources such asparticular aircraft operators (“pilots”), operations personnel, physicalresources available at a transportation node (e.g., machines for securepassenger intake, fuel, maintenance capabilities), etc. In someimplementations, particular pilots can be linked with particularaircraft and treated as a single resource while, in otherimplementations, they can be treated as separate resources.

In some implementations, the input data to the flight planning systemcan include other additional data such as forecasted demand, supply, orweather data. For instance, forecasted demand data can be provided as aninput to the flight planning system. The forecasted demand data candescribe a forecasted demand for flight transportation at various timesand locations over the flight planning period. The data can describeforecasted origins and destinations for the demand or may simplyforecast the origin of the demand.

The forecasted demand data can be based, at least in part, on the ridesharing network. The ride sharing network, for example, can include amulti-modal ride sharing network configured to provide a transportationservice (e.g., to be performed by aircraft, ground vehicle, etc.) for aplurality of different users of the network. For instance, the ridesharing network can receive a request for transportation between twolocations (e.g., an origin location and a desired location), determineone or more optimal modalities (e.g., aircraft, ground vehicles, etc.)for facilitating the transportation, and assign a service providerassociated with the one or more optimal modalities to provide thetransportation. The ride sharing network can determine forecasted demanddata by leveraging the number of real time requests and/or historicaldata indicative of the number of requests received from the plurality ofdifferent users of the network. In this manner, the forecasted demanddata can be determined based, at least in part, on the ride sharingnetwork. By way of example, the forecasted demand data can include thehistorical and/or real time requests from passengers of the ride sharingnetwork and/or data determined based, at least in part, on thehistorical and/or real time requests from passengers of the ride sharingnetwork and provided to the flight planning system. In this manner, theset of potential flights generated by the flight planning system canshift throughout an operational time period based, at least in part, oninformation from the ride sharing network.

As another example, forecasted supply data can be provided as an inputto the flight planning system. The forecasted supply data can describethe forecasted supply of ground-based transportation providers atdifferent respective times and transportation locations over the flightplanning period. The forecasted supply data can be based, at least inpart, on the ride sharing network. For instance, the ride sharingnetwork can include a plurality of service providers (e.g., drivers,aircraft operators, etc.) and/or be associated with a plurality of ridesharing assets (e.g., vehicles, etc.). The forecasted supply data canidentify a number of service providers and/or assets that are available(e.g., opted in, etc.) for providing a transportation service at aplurality of different times and/or locations over the flight planningperiod. In this manner, the forecasted supply data can be determinedbased, at least in part, on the ride sharing network. By way of example,the forecasted supply data can include historical and/or real timeinformation identifying the availability of the number of serviceproviders and/or assets of the ride sharing network and/or datadetermined based, at least in part, on the historical and/or real timeavailability of the number of service providers and/or assets of theride sharing network and provided to the flight planning system. In someimplementations, the set of potential flights generated by the flightplanning system can be provided as forecasted supply data for the ridesharing network. In such a case, the ride sharing network can prompt(e.g., by encouraging commitments from drivers, operators, and/or assetowners) the availability of the number of service providers and/orassets of the ride sharing network based, at least in part, on the setof potential flights.

As yet other examples, the additional data can include forecastedweather data that can be provided as input data to the flight planningsystem, available flight paths between infrastructure nodes, airspaceavailability throughout the planning period, and/or other aeronauticalinformation.

According to an aspect of the present disclosure, in response to theinput data, the flight planning system can generate a set of potentialflight plans for the fleet of aircraft over the flight planning period.Each potential flight plan can include various types of information suchas, as examples: the identity of the aircraft, the identity of the pilot(if needed), the origin location, the destination location, a roughindication of flight path, an estimated time of departure, an estimatedtime of arrival, and/or various other information regarding thepotential operation of an aircraft.

More particularly, the flight planning system can generate the set ofpotential flight plans that maximize satisfaction of a combination ofone or more objectives while also adhering to all of the constraints(e.g., not violating any of the constraints). As examples, theobjectives considered by the flight planning system can includemaximizing the number of potential flights generated, maximizing theratio of potential flight plans that become engaged, maximizing thenumber of engaged flights that operate at maximum passenger capacity,maximizing the number of passengers to whom a flight is delivered,maximizing the number of passengers that arrive at their destination ontime, minimizing the cumulative time period that passengers are delayedpast their desired arrival time, and/or various other objectives. Insome implementations, the flight planning system can use a fleet-levelobjective function that balances (e.g., using a set of weights) some orall of the different objectives described above. The set of weights canbe manually tuned and/or automatically tuned (e.g., learned).

The flight planning system can perform one or more of various differentalgorithms to generate the set of potential flight plans. For example,in some implementations, the flight planning system can iterativelyanalyze the fleet of aircraft on an aircraft-by-aircraft basis,generating an optimal set of potential flight plans for each aircraft inturn. For example, the optimal set of potential flight plans for anaircraft can be generated by optimizing, for such aircraft, anaircraft-level objective function that balances (e.g., using a set ofweights) a combination of any of the objectives described above. The setof weights in the aircraft-level objective function can be manuallytuned and/or automatically tuned (e.g., learned). In someimplementations, the aircraft-level objective function and thefleet-level objective function can be the same function and have thesame weights. In some implementations, the fleet-level objectivefunction can equal a sum of the aircraft-level objective function asrespectively applied to all aircraft in the fleet.

In particular, in one example, the flight planning system can generate aset of flight plans for a particular aircraft by starting at thebeginning of the flight planning period and sequentiallygenerating/adding flight plans for the aircraft until the end of theflight planning period is reached. As one example, at each instance inwhich the flight planning system attempts to generate a new flight planfor an aircraft, the flight planning system can generate a plurality ofcandidate flight plans, score each candidate flight plan according tothe aircraft-level objective function, and then select and add thehighest scoring flight plan for the aircraft.

As described above, the aircraft-level objective function can balanceany number of different objectives, including, as examples, variousobjectives which are a function of demand for the generated flight plan(e.g., total number of passengers serviced, total number of engagedflights, total number of flights that have all available seats filled,etc.). To evaluate such an objective function that includes objectivesthat are a function of demand, the flight planning system can query ademand model to obtain forecasted demand and/or supply information thatcan be used to evaluate such objectives. In another example, theaircraft-level objective function may focus (e.g., include as the soleobjective) on maximizing the total number of flight plans generated.

In some implementations, the objective function can evaluate a set offlight plans based, at least in part, on an ultimate journey for aplurality of potential passengers. For example, an objective can includea function to reduce the total travel time for the plurality ofpassengers. By way of example, the demand data (e.g., received and/orbased on the ride share service) can include an anticipated demand fortransportation services. At times, the transportation services caninclude multi-modal transportation services. In such a case, an aerialflight leg such as one of the set of potential flight plans can includeone of multiple destinations for a passenger before the passengerreaches a final destination. The objective function can evaluate the setof flight plans based, at least in part, on the total estimated traveltime for the plurality of passengers. For example, the flight planningsystem can identify infrastructural, traffic, weather, and/or otherfactors that can have an impact on a subsequent ground transportationleg and optimize the set of potential flight plans to lower the totalestimated travel time for users of the ride sharing network.

By way of example, the flight planning system can take ground-basedtransportation information into account when generating the set offlight plans. For example, forecasted last mile supply availability canbe used as an input to the aircraft-level objective function to reward(e.g., increase objective score for) flights that deliver passengers todestinations which have a robust supply of ground-based transportationservices and to penalize (e.g., decrease objective score for) flightsthat deliver passengers to destinations which do not have a robustsupply of ground-based transportation services. Similarly, forecastedfirst mile supply availability can be used as an input to theaircraft-level objective function to reward (e.g., increase objectivescore for) flights that collect passengers from departure nodes whichhave a robust supply of ground-based transportation services and topenalize (e.g., decrease objective score for) flights that collectpassengers from departure nodes which do not have a robust supply ofground-based transportation services. In other implementations, suchinformation regarding supply of transportation services according toother modalities can be assumed to be included in the forecasted demanddata.

The flight planning system (e.g., the objective function, etc.) canevaluate the set of flight plans based, at least in part, on how closethe set of potential flight plans can transport potential passengers(e.g., expected based on forecasted demand data, etc.) to an expectedultimate destination. For example, the forecasted demand data caninclude an expected ultimate destination for each of a plurality ofanticipated passengers. The flight planning system can generate the setof potential flight plans in order to facilitate the transport of theplurality of anticipated passengers to a location (e.g., a transportnode) closest to the expected ultimate destination.

In some implementations, the closest location (e.g., transport node) tothe expected ultimate destination may not be the most efficient and/ortimely route for transporting the plurality of anticipated passengers.For example, a subsequent ground transportation from the closestlocation can be affected by traffic and/or other roadway delays.Moreover, in some implementations, the flight plan to the closestlocation can be affected by weather or other airspace delays. In such acase, the flight planning system (e.g., the objective function, etc.)can generate/evaluate the set of flight plans in order to facilitate thetransport of the plurality of anticipated passengers to a location(e.g., a transport node) closest, temporally, to the expected ultimatedestination such that factors affecting the subsequent transportation toan ultimate destination are accounted for.

In some implementations, the flight planning system can perform a tieredapproach to generating flight plans. In particular, the flight planningperiod can be divided into a number of different time periods and eachtime period can be assigned to one of a plurality of priority tiers.There can be any number of priority tiers including, for example, twotiers. More particularly, the tiered approach recognizes that certaintime periods through the day or week may be higher priority due to anincreased level of demand by passengers. As one example, time periodscorresponding to “rush hours” or common commuting times can beconsidered higher priority than other time periods. As another example,time periods which include popular events (e.g., sporting events, musicfestivals, and/or the like) occur can be recognized as higher prioritytime periods. In yet another example, the time periods can be sortedinto the different priority tiers based on the forecasted demand data.

In the multi-tiered approach, the flight planning system can iterativelygenerate flight plans for the time periods in each priority tier,starting with the highest priority tier first. Thus, after determiningflight plans for the time periods of the highest priority tier, theflight planning system can then determine flight plans for the timeperiods of the next-highest priority tier. In particular, each tier oftime periods can use the generated flight plans for the previous tier(s)as constraints for the scheduling process. For example, if the generatedflight plans for the time period of 4 pm to 7 pm require that a givenaircraft start at a particular transportation node and with a particularfuel/charge level, then, when generating flight plans for the timeperiod of 1 pm to 4 pm, the flight planning system can treat asconstraints the fact that the aircraft needs to end the 1-4 pm timeperiod at the particular transportation node and with the particularfuel/charge level. In such fashion, flight plans can be optimizedspecifically for the highest priority time periods in which, forexample, the largest number of passengers need to be serviced.

In addition, in some implementations, the flight planning algorithms caninclude a greedy refueling component. The greedy refueling component cangreedily assign an aircraft to participate in refueling and/orrecharging whenever the aircraft is not in-flight (e.g., for more than aminimum amount of time) and refueling/recharging infrastructure isavailable at the aircraft's current location. The greedy refuelingcomponent can be applied after generation of the flight plans or can beused as part of the generation process itself. In such fashion, the useof refueling/recharging infrastructure can be optimized.

In some implementations, the flight planning system can perform orparticipate in an iterative learning process for weights of planningobjective function(s). For example, an initial set of weights for theobjective function(s) can be manually tuned. The flight planning systemcan operate with the initial set of weights to generate any number offlight plans for any number of flight planning periods. Outcomesassociated with the generated flight plans can be observed and measuredaccording to various metrics. The set of weights can be retuned (e.g.,automatically and/or manually) based on the observed outcomes. Forexample, various learning techniques such as gradient descent techniquescan be applied to learn the set of weights. For example, gradientdescent techniques can be applied to the weights of fleet-levelobjective function or the aircraft-level objective function. Moregenerally, the flight planning system can collect any data thatdescribes the outcomes of certain manually-controlled settings, actions,weightings, decisions, and/or the like and can apply machine learningtechniques to such data to learn updated or optimized versions of suchsettings, actions, weightings, decisions, and/or the like.

In this manner, the flight planning system can automatically generatethe set of potential flight plans for a fleet of aircraft owned and/oroperated by one or more different aerial operators. In someimplementations, each flight plan can be approved by a respective aerialvehicle operator. For example, each aerial vehicle operator can beassociated with a preapproval rule set. The preapproval ruleset, forexample, can outline one or more acceptable flight plans (e.g.,from/to/between one or more approved locations, within an approved timeperiod, etc.) for aerial vehicles under the operational control of therespective aerial vehicle operator. In some implementations, the set ofpotential flight plans can only include approved flight plans. Inaddition, or alternatively, the set of potential flight plans caninclude unapproved flight plans. In such a case, the unapproved flightplans can be provided (e.g., escalated) to the respective aerial vehicleoperator for manual approval. The flight planning system can modifyand/or regenerate any flight plan that is not manually and/orpreapproved by a respective aerial vehicle operator.

After the flight planning system generates the set of potential flightplans for the fleet and flight planning period, the set of potentialflight plans can be exposed to a ride sharing network. In particular, amatching system of the ride sharing network can match one or morepassengers of the network with a particular flight plan according to amatching process, thereby causing the flight plan to be engaged foroperation. In some implementations, the matching system can performpassenger pooling in which multiple requests for service are collectedfrom users over a period and the matching services collectively analyzesthe requests to identify opportunities to pool passengers together.Although aspects of the present disclosure focus on providing aerialtransportation services to human passengers, the systems and methods ofthe present disclosure are equally applicable to determining flightplans for aerial transportation services to non-human payloads, such aspackages, prepared foods, pet transportation, and/or the like.

In some implementations, the set of potential flight plans can beexposed to the ride sharing network with minimal details. For example,the potential flight plans can be described by data indicative of anumber of passengers and/or trips that can be serviced during a block oftime at, between, and/or to a plurality of locations. The passengers ofthe ride sharing network can book a trip by booking an aerial transportover a block of time. The booked block of time can be utilized by theride sharing network to match a passenger to a flight from the potentialset of flights.

By way of example, passengers of the ride sharing network can booktravel (e.g., generally, flight specific, etc.) for a block of time. Theblock of time can be descriptive of a time slot container and can beused as an input for generating an engaged flight plan. The ride sharingnetwork can receive a plurality of requests, group the plurality ofrequests within one or more time slot containers, and wrap a flightaround the time slot container. The passengers can be given a priceestimate, but not be charged until after the performance of the flight.Before the performance of the flight, the passenger can be providedinformation such as an aircraft type assigned to perform the flight, atime slot associated with the flight, and/or any other informationassociated with the engaged flight.

In this manner, an engaged flight plan can be generated by addingpassengers to a potential flight plan of the set of potential flightplans. Thus, an engaged flight plan can include a potential flight planwith one or more assigned passengers. For example, each engaged flightplan can be associated with one or more assigned passengers of thepassengers associated with the ride sharing network. In addition, theengaged flight plan can include a scheduled take-off time, a scheduledlanding time, and a buffer time period. The buffer time period can beindicative of a delay from the scheduled take-off time. This can be, forexample, a time period that is understood to be acceptable topassengers. Acceptability can be based on feedback data provided viapassenger(s) through a software application running on a user device.For example, the software application can provide a prompt to gatherfeedback from the passenger regarding the passenger's experienceincluding, for example, the passenger's satisfaction or dissatisfactionlevel with a delay. Feedback data from specific passengers can be storedfor a determination of acceptability for a particular passenger (e.g.,which can be if they are included on a later flight) and/or aggregatedwith feedback data from one or more other passenger(s) to determinewhether a time period would represent an acceptable delay to otherpassenger(s). In some implementations, the aggregated data can beutilized for determining an acceptable delay for a passenger thatpreviously provided feedback data or not. The buffer time period can bedetermined based, at least in part, on the input data, the one or moreconstraints (e.g., weather, traffic, deviations, etc.), and/or the timeperiod. For example, the engaged flight plan can form a user-centricflight schedule with an adjustable take-off time based, at least inpart, on the assigned passengers associated with the engaged flightplan. This adjustable, user-centric, take-off time can be represented bythe buffer time period.

More particularly, the engaged flight plan can include a buffer timeperiod determined based on holistic inputs centered around passengerconvenience. By way of example, the flight planning system can generatean engaged flight plan based, at least in part, on one or more assignedpassengers (e.g., added by the ride sharing network). The flightplanning system can determine a buffer time period for the engagedflight plan based, at least in part, on the one or more assignedpassengers of the engaged flight plan.

For example, each of the one or more assigned passengers can beassociated with a multi-leg travel itinerary. The multi-leg travelitinerary can include, for example, multiple travel legs via one or moredifferent modalities. For instance, one leg of the multi-leg travelitinerary can include the engaged flight plan. Additional travel legscan include ground transportation to an origin location of the engagedflight plan, another ground transportation from the destination locationof the engaged flight plan, and/or one or more additional flight legspreceding and/or subsequent to the engaged flight plan.

In some implementations, the flight planning system can determine thebuffer time period based, at least in part, on the multi-leg travelitinerary for each of the one or more assigned passengers. For instance,the multi-leg travel itinerary can be associated with a total estimatedtravel time for an assigned passenger. In such a case, the buffer timeperiod can be determined based, at least in part, on an aggregated delayperiod to the multi-leg travel itinerary of each of the one or moreassigned passengers as a result of delaying the engaged flight plan byone or more different periods of time. The buffer time period, forexample, can be determined to ensure that the aggregated delay period isless than a threshold time (e.g., one or more hour(s), 30 minutes, 10minutes, etc.). In some cases, the threshold time can include a timeperiod less than the duration of an engaged flight. As one example, theflight planning system can predict a higher rate of traffic for groundtransportation a first time period after the engaged flight. In such acase, the flight planning system can determine a buffer time period thatis less than the first time period to prevent the affected passengerfrom being further delayed by foreseeable traffic.

In addition, or alternatively, the buffer time period can be determinedbased, at least in part, on a number of the one or more assignedpassengers that will have a change to their multi-leg travel itineraryas a result of delaying the engaged flight plan by one or more differentperiods of time. For example, the buffer time period can be determinedto avoid causing one or more assigned passengers to miss a subsequentflight.

A computing system that includes the flight planning system cancontinuously monitor the success/viability of each engaged flight plan.For example, the computing system can monitor real-time data such asaircraft location data (e.g., received from a GPS system of theaircraft), aircraft sensor data (e.g., fuel/charge levels, etc.),passenger location data (e.g., with permission, received from apassenger's computing device), weather data, ground-based transportationdata, and/or other forms of data to detect current or likely deviationsof the fleet of aircraft and/or passengers from engaged flight plans. Inparticular, the computing system can monitor and evaluate the estimatedtimes of arrival for some or all passengers versus planned times ofarrival, the estimated times of arrival for some or all aircraft versusplanned times of arrival, and/or other measures of success of flightplans.

Thus, the computing system can continuously assess whether flights willsuccessfully depart and/or arrive at their scheduled times, includingtracking whether assigned passengers will be able to successfully arriveat the departing transportation node in sufficient time to physicallyprogress through the transportation node and embark on the aircraft. Inparticular, in some implementations, the estimated arrival time can befor a passenger and can be based on a first leg of a multi-legitinerary. For example, a user may use ground-based transportation(e.g., a ride shared car) to get to the transportation node and, assuch, the computing system can track the user's progress along theground-based transportation leg to assess whether the user will arrivein sufficient time to avoid delaying their associated flight (e.g.,which may be the second leg of the multi-leg itinerary).

The computing system can track the user location, for example, byreceiving location data associated with one or more of the one or morepassengers. For example, the computing system can interact with the ridesharing network to receive updates to a passenger's location. As anexample, the computing system can receive passenger location dataassociated with a respective passenger from a ground vehicle deviceassigned to the respective passenger for providing ground transportationto the respective passenger before a respective engaged flight plan forthe passenger. The location data can be received in real-time,periodically, during the course of travel from the passenger's originlocation to the first transportation node. In addition, oralternatively, the location data can be received in response to adetected deviation from an original estimated time of arrival. Forexample, the location data for a passenger can be indicative of adelayed or earlier estimated time of arrival for a passenger. In someimplementations, the location data can include traffic information,driver information, flight information, and/or any other informationassociated with a passenger's transportation to a first transportationnode of the engaged flight.

The computing system can perform real-time mitigation and replanningwhen a particular flight plan becomes significantly delayed orcancelled/unfulfilled. Typically, the computing system can attempt todelay replanning activities until it is believed with significantprobability that an engaged flight plan will not be able to besuccessfully completed.

In addition, or alternatively, the computing system can performreal-time mitigation and replanning based, at least in part, on one ormore deviations of the one or more passengers. For example, thecomputing system can reoptimize the set of potential flight plans and/orthe one or more engaged flight plans based, at least in part, onminimizing inconveniences to passengers. By way of example, thecomputing system can determine a mitigation action (e.g., delay aflight, reassign one or more passengers to different engaged flights,etc.) in response to one or more deviations of the fleet of aircraftand/or passengers. For each available mitigation action, the computingsystem can determine a number of passengers that will be impacted as aresult of the mitigation action. By way of example, the computing systemcan determine a number of passengers that will miss their arrive bytimes as a result of the mitigation action, an aggregate time period(e.g., one or more hours, minutes, etc.) that will be added to all thepassengers' transportation services as a result of the mitigationaction, and/or other metrics. In this manner, the computing system canadjust one or both of the engaged flight plans and/or the set ofpotential flight plans to account for the one or more deviations withoutnegatively impacting the convenience of passengers engaged in an ondemand transportation service.

In some implementations, the mitigation process can include delaying aflight based on the buffer time period of an engaged flight plan. Forexample, the buffer time period can be indicative of a time periodbefore and/or after a scheduled take-off that is allowable for theaircraft. For example, allowances can be made for accommodating a lateand/or early passenger based, at least in part, on an understanding ofthe user (e.g., location thereof) and other pooled users associated withthe engaged flight. For example, the buffer time period can bedetermined based on a group inconvenience factor and/or a trickle downimpact of a delay take-off time. The computing system can determine thata deviation is due to a late assigned passenger for an engaged flightplan. The computing system can determine an estimated time of arrivalfor the late assigned passenger and determine an adjustment for theengaged flight plan based, at least in part, on the estimated time ofarrival for the late assigned passenger and the buffer time period. Thecomputing system can apply the adjustment to the engaged flight plan.

By way of example, in response to determining that the estimated time ofarrival for the late assigned passenger is after the scheduled take-offtime by a time period greater than the buffer time period, the computingsystem can add the late assigned passenger to one or more of the set ofpotential flight plans (and/or engaged flight plans with space for thelate assigned passenger). In addition, or alternatively, in response todetermining that the estimated time of arrival for the late passenger isafter the scheduled take-off time and within the buffer time period, thecomputing system can delay the scheduled take-off time for the engagedflight plan to accommodate the late assigned passenger. In someimplementations, the computing system can automatically transmitnotifications to one or more of: the late assigned passenger (e.g., viaa user device), operations personnel (e.g., via operational devices),and/or aircraft operators (e.g., via aircraft devices).

In some implementations, the mitigation process can include manualinputs by human mitigation personnel. For example, the need to performmitigation can be automatically detected and, as a result, the computingsystem can provide an alert and a mitigation user interface to a humanmitigation personnel. For example, the mitigation user interface caninclude a graphical user interface that shows potential alternativeflight plans for and/or changes to a flight plan that is currentlysubject to delay/cancellation. As one example, the human personnel caninteract with the interface to adjust various parameters of one or moreflight plans. For example, the human personnel can alter flightdeparture times, alter buffer times associated with physical passage ofpassengers through transportation nodes and/or vehicleembarkation/disembarkation, add or remove passengers from flights, movepassengers between flights, change pilots, and/or other actions tomanually alter the set of flight plans. In some implementations, anydownstream effects on the flight plans from a manual change can beautomatically computed and propagated through the set of flight plans.

In some implementations, the user interface can provide warnings orother indications of how certain mitigation activities or potentialactions might affect other users of the system and/or violate certain ofthe initial input constraints. For example, if the mitigation personnelattempts to delay a not-yet-departed flight plan to wait for a delayeduser, the user interface can inform the mitigation personnel that suchaction would impact one or more other travelers (e.g., 3 othertravelers). The warnings/indications provided in the user interface canprovide impact information according to various metrics including, foreach available choice/action, a number of users that will be impacted asa result of the choice/action, a number of users that will miss theirarrive by times as a result of the choice/action, an aggregate timeperiod that will be added to all the users' transportation services as aresult of the choice/action, and/or other metrics. Generally, preferencecan be given to mitigation strategies that have minimal impacts on otherpassengers. In addition, certain constraints (e.g., final aircraftdestination at the end of the flight planning period) can be manuallyviolated while other constraints (e.g., safety constraints such asmaximum weight in the aircraft) cannot be manually violated.

In another example, a human personnel can change one or more constraintsand then rerun the flight planning process. For example, if extremeweather renders a certain subset of the transportation nodes unusablefor a period of time, the human personnel can adjust the constraints tomark the subset of transportation nodes as unavailable for the period oftime and then rerun the flight planning process. Thus, certainadjustments can be made directly to individual flight plans while otheradjustments may require a system-wide readjustment or replanning.

In some implementations, the mitigation process can be automated (e.g.,with the ability for manual overrides). As an example, the computingsystem can continuously generate contingency flight plans. For example,the contingency flight plans can be generated using a process asdescribed above but taking into account potential or actual delays incertain flight plans. When it is detected that mitigation interventionsshould be performed, the computing system can automatically select thebest available contingency flight plans and push the selected flightplans out to each aircraft and other system component. For example,automatic updates and alerts can be sent to passengers, aircraftproviders, operations personnel, and/or other integrated systems. Thecontingency flight plans can be ranked based on various metricsincluding, for each potential set of updated flight plans, a number ofpassengers that will be impacted as a result of the choice/action, anumber of passengers that will miss their arrive by times as a result ofthe choice/action, an aggregate time period that will be added to allthe passengers' transportation services as a result of thechoice/action, and/or other metrics. Alternatively, or additionally, theobjective function(s) can be used to score the contingency flight plansand/or replan for some or all of the fleet of aircraft. Thus, in someinstances, the dynamic contingency generation can be viewed as acontinuous fleet-wide reoptimization of flight plans based on real-timeconditions.

In some implementations, the mitigation process can be automated andimplemented within the bounds of preapproval criteria established by theone or more aircraft operators. For example, the computing system candetect one or more deviations of the fleet of aircraft and/or the one ormore passengers from the engaged flight plans. The computing system candetermine an adjustment for at least one of the engaged flight plansand/or the set of potential flight plans to accommodate for the one ormore deviations. The computing system can compare the adjustment topreapproval criteria for an operator of the at least one of the engagedflight plans or the set of potential flight plans. In response todetermining that the adjustment achieves the preapproval criteria, thecomputing system can automatically adjust the at least one of theengaged flight plan and/or the set of potential flight plans. Inresponse to determining that the adjustment does not achieve thepreapproval criteria, the computing system can transmit data indicativeof the adjustment to human mitigation personnel.

Thus, systems and methods of the present disclosure can generatepotential flight plans to be used by a ride sharing network, including,in some implementations, dynamic and/or automated changes to flightplans based on real-time information. In particular, the systems andmethods of the present disclosure can operate to generate a fleet-levelset of potential flight plans which comply with one or more constraintsfor a fleet of aircraft and can provide manual and/or automatic toolsfor flight plan adjustment to, for example, handle and mitigate delaysor other real-time impacts that cause deviation of the fleet of aircraftfrom the set of flight plans.

With reference now to the Figures, example embodiments of the presentdisclosure will be discussed in further detail.

Example Devices and Systems

FIG. 1 depicts a block diagram of an example computing system 100according to example embodiments of the present disclosure. Thecomputing system 100 includes a cloud services system 102 that canoperate to plan and fulfill transportation services.

The cloud services system 102 can be communicatively connected over anetwork 180 to one or more rider computing devices 140, one or moreservice provider computing devices 150 for a first transportationmodality, one or more service provider computing devices 160 for asecond transportation modality, one or more service provider computingdevices 170 for an Nth transportation modality, and one or moreinfrastructure and operations computing devices 190.

Each of the computing devices 140, 150, 160, 170, 190 can include anytype of computing device such as a smartphone, tablet, hand-heldcomputing device, wearable computing device, embedded computing device,navigational computing device, vehicle computing device, etc. Acomputing device can include one or more processors and a memory (e.g.,similar to as will be discussed with reference to processors 112 andmemory 114). Although service provider devices are shown for N differenttransportation modalities, any number of different transportationmodalities can be used, including, for example, less than the threeillustrated modalities (e.g., two modalities can be used). Serviceproviders can include human operators of vehicles or the vehiclesthemselves.

The cloud services system 102 includes one or more processors 112 and amemory 114. The one or more processors 112 can be any suitableprocessing device (e.g., a processor core, a microprocessor, an ASIC, aFPGA, a controller, a microcontroller, etc.) and can be one processor ora plurality of processors that are operatively connected. The memory 114can include one or more non-transitory computer-readable storage media,such as RAM, ROM, EEPROM, EPROM, one or more memory devices, flashmemory devices, etc., and combinations thereof.

The memory 114 can store information that can be accessed by the one ormore processors 112. For instance, the memory 114 (e.g., one or morenon-transitory computer-readable storage mediums, memory devices) canstore data 116 that can be obtained, received, accessed, written,manipulated, created, and/or stored. In some implementations, the cloudservices system 102 can obtain data from one or more memory device(s)that are remote from the system 102.

The memory 114 can also store computer-readable instructions 118 thatcan be executed by the one or more processors 112. The instructions 118can be software written in any suitable programming language or can beimplemented in hardware. Additionally, or alternatively, theinstructions 118 can be executed in logically and/or virtually separatethreads on processor(s) 112. For example, the memory 114 can storeinstructions 118 that when executed by the one or more processors 112cause the one or more processors 112 to perform any of the operationsand/or functions described herein. By way of example, the memory 114 caninclude instructions 118 that when executed by the one or moreprocessors 112 cause the one or more processors 112 to performoperations of the flight planning system described herein.

The cloud services system 102 can include a number of different systemssuch as a world state system 126, a forecasting system 128, anoptimization/planning system 130, and a matching and fulfillment system132. The matching and fulfillment system 132 can include a differentmatching system 134 for each transportation modality and a monitoringand mitigation system 136. Each of the systems 126-136 can beimplemented in software, firmware, and/or hardware, including, forexample, as software which, when executed by the processors 112 causethe cloud services system 102 to perform desired operations. The systems126-136 can cooperatively interoperate (e.g., including supplyinginformation to each other).

The world state system 126 can operate to maintain data descriptive of acurrent state of the world. For example, the world state system 126 cangenerate, collect, and/or maintain data descriptive of predicted riderdemand; predicted service provider supply; predicted weather conditions;planned itineraries; predetermined transportation plans (e.g., flightplans) and assignments; current requests; current ground transportationservice providers; current transportation node operational statuses(e.g., including recharging or refueling capabilities); current aircraftstatuses (e.g., including current fuel or battery level); currentaircraft pilot statuses; current flight states and trajectories; currentairspace information; current weather conditions; current communicationsystem behavior/protocols; and/or the like. The world state system 126can obtain such world state information through communication with someor all of the devices 140, 150, 160, 170, 190. For example, devices 140can provide current information about riders while devices 150, 160, and170 can provide current information about service providers. Devices 190can provide current information about the status of infrastructure andassociated operations/management.

The forecasting system 128 can generate predictions of the demand andsupply for transportation services at or between various locations overtime. The forecasting system 128 can also generate or supply weatherforecasts. The forecasts made by the system 128 can be generated basedon historical data and/or through modeling of supply and demand. In someinstances, the forecasting system 128 can be referred to as an RMRsystem, where RMR refers to “routing, matching, and recharging.” The RMRsystem can be able to simulate the behavior of a full day of activityacross multiple ride share networks.

The optimization/planning system 130 can generate transportation plansfor various transportation assets and/or can generate itineraries forriders. For example, the optimization/planning system 130 can performflight planning for a fleet of aircraft (e.g., through performance ofany of the methods described herein including methods 400, 500, and 600of FIGS. 4, 5, and 6). As another example, optimization/planning system130 can plan or manage/optimize itineraries which include interactionsbetween riders and service providers across multiple modes oftransportation.

The matching and fulfillment system 132 can match a rider with a serviceprovider for each of the different transportation modalities. Forexample, each respective matching system 134 can communicate with thecorresponding service provider computing devices 150, 160, 170 via oneor more APIs or connections. Each matching system 134 can communicatetrajectories and/or assignments to the corresponding service providers.Thus, the matching and fulfillment system 132 can perform or handleassignment of ground transportation, flight trajectories,take-off/landing, etc.

The monitoring and mitigation system 136 can perform monitoring of useritineraries and can perform mitigation when an itinerary is subject tosignificant delay (e.g., one of the legs fails to succeed). Thus, themonitoring and mitigation system 136 can perform situation awareness,advisories, adjustments, and the like. The monitoring and mitigationsystem 136 can trigger alerts and actions sent to the devices 140, 150,160, 170, and 190. For example, riders, service providers, and/oroperations personnel can be alerted when a certain transportation planhas been modified and can be provided with an updated plan/course ofaction. Thus, the monitoring and mitigation system 136 can haveadditional control over the movement of aircraft, ground vehicles,pilots, and riders.

In some implementations, the cloud services system 102 can also store orinclude one or more machine-learned models. For example, the models canbe or can otherwise include various machine-learned models such assupport vector machines, neural networks (e.g., deep neural networks),decision-tree based models (e.g., random forests), or other multi-layernon-linear models. Example neural networks include feed-forward neuralnetworks, recurrent neural networks (e.g., long short-term memoryrecurrent neural networks), convolutional neural networks, or otherforms of neural networks.

In some instances, the service provider computing devices 150, 160, 170can be associated with autonomous vehicles. Thus, the service providercomputing devices 150, 160, 170 can provide communication between thecloud services system 102 and an autonomy stack of the autonomousvehicle which autonomously controls motion of the autonomous vehicle.

The infrastructure and operations computing devices 190 can be any formof computing device used by or at the infrastructure or operationspersonnel including, for example, devices configured to performpassenger security checks, luggage check in/out, recharging/refueling,safety briefings, vehicle check in/out, and/or the like.

The network(s) 180 can be any type of network or combination of networksthat allows for communication between devices. In some embodiments, thenetwork(s) can include one or more of a local area network, wide areanetwork, the Internet, secure network, cellular network, mesh network,peer-to-peer communication link and/or some combination thereof and caninclude any number of wired or wireless links. Communication over thenetwork(s) 180 can be accomplished, for instance, via a networkinterface using any type of protocol, protection scheme, encoding,format, packaging, etc.

Example Fixed Infrastructure

FIG. 2 depicts a graphical diagram of an example set of flight plansbetween an example set of transportation nodes according to exampleembodiments of the present disclosure. In particular, FIG. 2 provides asimplified illustration of an example fixed infrastructure associatedwith flight-based transportation in an example metropolitan area. Asillustrated in FIG. 2, there are four transportation nodes which may bereferred to as “skyports” (e.g., vertiports, verti-hubs, etc.) Forexample, a first transportation node 202 is located in a firstneighborhood of the metropolitan area, a second transportation node 204is located in a second neighborhood, a third transportation node 206 islocated in a third neighborhood, and a fourth transportation node 208 islocated in a fourth neighborhood. The location and number oftransportation nodes is provided only as an example. Any number oftransportation nodes at any different locations can be used.

Flights are available (e.g., may be preplanned) between certain pairs ofthe transportation nodes. For example, a flight path 210 exists betweenthe first transportation node 202 and the fourth transportation node208. Likewise, a flight path 212 exists between the fourthtransportation node 208 and the third transportation node 206.

FIG. 3 depicts a graphical diagram of an example transportation node 300according to example embodiments of the present disclosure. The exampletransportation node 300 includes a number of take-off/landing pads suchas pads 302 and 304. The example transportation node 300 also includes anumber of vehicle parking locations such as parking locations 306 and308. For example, refueling or recharging infrastructure may beaccessible at each parking location.

Flight trajectories into and out of the transportation node 300 may bedefined, configured, assigned, communicated, etc. FIG. 3 illustrates anumber of flight trajectories including, for example, trajectories 310and 312. The trajectories can be fixed or can be dynamically computed.The trajectories can be computed by the aircraft or can be centrallycomputed and then assigned and communicated to the aircraft. As oneexample, FIG. 3 illustrates a helicopter 314 taking off from the pad 304according to the trajectory 312.

Example Methods

FIG. 4 depicts a flow chart diagram of an example method 400 to generateand use a set of flight plans within a ride sharing network according toexample embodiments of the present disclosure. One or more portion(s) ofthe method 400 can be implemented by a computing system that includesone or more computing devices such as, for example, the computingsystems described with reference to the other figures (e.g., flightplanning system, computing system 100, cloud services system 102, worldstate system(s) 126, forecasting system 128, optimization/planningsystem 130, matching and fulfillment system 132, etc.). Each respectiveportion of the method 400 can be performed by any (or any combination)of one or more computing devices. Moreover, one or more portion(s) ofthe method 400 can be implemented as an algorithm on the hardwarecomponents of the device(s) described herein (e.g., as in FIGS. 1-3,etc.), for example, to generate a set of flight plans. FIG. 4 depictselements performed in a particular order for purposes of illustrationand discussion. Those of ordinary skill in the art, using thedisclosures provided herein, will understand that the elements of any ofthe methods discussed herein can be adapted, rearranged, expanded,omitted, combined, and/or modified in various ways without deviatingfrom the scope of the present disclosure. FIG. 4 is described withreference to elements/terms described with respect to other systems andfigures for exemplary illustrated purposes and is not meant to belimiting. One or more portions of the method 400 can be performedadditionally, or alternatively, by other systems.

At (402), the method 400 can include receiving data descriptive of afleet of aircraft and one or more flight planning constraints for aflight planning period. For example, a computing system (e.g., computingsystem 100, cloud services system 102, world state system(s) 126,forecasting system 128, optimization/planning system 130, matching andfulfillment system 132) can receive data descriptive of a fleet ofaircraft and one or more flight planning constraints for a flightplanning period. The computing system (e.g., the optimization andplanning system 130, etc.) can receive a set of input data thatdescribes a fleet of aircraft, one or more flight planning constraints,and/or a flight planning period. The input data can be provided by oneor more systems and/or devices of a ridesharing environment. Forinstance, the input data can include rider data received via one or morerider computing device(s) 140 of FIG. 1, service provider data receivedfrom one or more service provider devices 150, 160, 170, of FIG. 1,infrastructure data received from one or more infrastructure andoperations computing devices 190, world state data received from one ormore world state systems 126, forecasting data received from one or moreforecasting systems 128, and/or any other information associated with aride sharing network.

The user input data can be input manually by a user (e.g., passenger,pilot, driver, operations personnel, etc.) and/or can be automaticallyingested (e.g., periodically such as daily). For example, in someimplementations, disparate aircraft owners/operators can interact (e.g.,via service provider device(s) 150, 160, 170, etc.) with the computingsystem (e.g., application implemented thereby) to provide information onthe availability of their aircraft for participation in the ride sharingnetwork. The provided information can then be accessed by the computingsystem (e.g., optimization/planning system 130). In otherimplementations, information about the fleet of available aircraft comesfrom a single centralized source that oversees all aircraft operations.

The fleet of aircraft can include any number of aircraft of the same ordifferent models owned and/or operated by one or more different aerialoperators. Example aircraft that can be included in the fleet includehelicopters or other vertical take-off and landing aircraft (VTOL) suchas electric vertical take-off and landing aircraft (eVTOL). In someimplementations, the fleet of aircraft can include aircraft that arecapable of varying levels of autonomous flight/motion, including,non-autonomous aircraft, semi-autonomous aircraft, and full-autonomousaircraft. Each aircraft can be owned, maintained, and/or operated by oneor more different aerial vehicle operators. By way of example, an aerialvehicle operator can include any entity with operational control (e.g.,ownership, license, etc.) over one or more aerial vehicles. Each aerialvehicle operator can make the one or more aerial vehicles availableduring the flight planning period.

The flight planning period can define an overall start and end date andtime for which the flight planning system should generate potentialflight plans for the fleet of aircraft. Example flight planning periodsinclude spans of 3 hours, 24 hours, a week, etc. The flight planningperiod can be continuous or include disjointed periods of time.

The constraints described by the set of input data can include anynumber of different constraints associated with each aircraft such as,as examples: a respective starting location for each aircraft; arespective ending location for each aircraft; respective times at whicheach aircraft is available or unavailable; respective starting fuel orcharge levels for each aircraft; respective capabilities or attributesof each aircraft such as number of passenger seats available, maximumconsecutive operational time, maximum or minimum flight range, maximumor minimum flight altitude, noise levels associated with operation ofthe aircraft, etc.

The constraints described by the set of input data can also describe afixed infrastructure (e.g., as such as fixed infrastructure 300 of FIGS.2-3) that the fleet of aircraft must utilize. In some implementations,the constraints described by the set of input data can also describe anavailability of resources such as particular aircraft operators(“pilots”), operations personnel, physical resources available at atransportation node (e.g., machines for secure passenger intake, fuel,maintenance capabilities), etc. In some implementations, particularpilots can be linked with particular aircraft and treated as a singleresource while, in other implementations, they can be treated asseparate resources.

In some implementations, the input data to the computing system (e.g.,optimization/planning system 130) can include world state and/orforecasted data (e.g., as determined/predicted by the world state system126 and/or the forecasting system 128). For example, the input data caninclude forecasted demand data that can describe a forecasted demand forflight transportation at various times and locations over the flightplanning period. The data can describe forecasted origins anddestinations for the demand or may simply forecast the origin of thedemand.

The forecasted demand data can be based, at least in part, on the ridesharing network. The ride sharing network (e.g., one or more device(s)102, 140, 150, 160, 170, 190 thereof) can determine forecasted demanddata by leveraging data indicative of a number of real time requestsand/or historical data indicative of the number of requests receivedfrom the plurality of different users (e.g., drivers, service providers,pilots, operational personnel, etc.) of the network. In this manner, theforecasted demand data can be determined based, at least in part, on theride sharing network. By way of example, the forecasted demand data caninclude the historical and/or real time requests from passengers of theride sharing network and/or data determined based, at least in part, onthe historical and/or real time requests from passengers of the ridesharing network and provided to the computing system (e.g., forecastingsystem 128).

As another example, the input data can include forecasted supply datathat can describe the forecasted supply of ground-based transportationproviders at different respective times and transportation locationsover the flight planning period. The forecasted supply data can bebased, at least in part, on the ride sharing network. For example, theride sharing network (e.g., one or more device(s) 102, 140, 150, 160,170, 190 thereof) can determine the forecasted supply data byidentifying a number of service providers and/or assets that areavailable (e.g., opted in, etc.) for providing a transportation serviceat a plurality of different times and/or locations over the flightplanning period. In this manner, the forecasted supply data can bedetermined (e.g., by the forecasting system 128). By way of example, theforecasted supply data can include historical and/or real timeinformation identifying the availability of the number of serviceproviders and/or assets of the ride sharing network and/or datadetermined based, at least in part, on the historical and/or real timeavailability of the number of service providers and/or assets of theride sharing network and provided to the computing system (e.g.,optimization/planning system 130).

At (404), the method 400 can include generating a set of potentialflight plans for the fleet of aircraft. For example, the computingsystem (e.g., computing system 100, cloud services system 102, worldstate system(s) 126, forecasting system 128, optimization/planningsystem 130, matching and fulfillment system 132) can generate a set ofpotential flight plans for the fleet of aircraft. For instance, inresponse to the input data, the computing system (e.g., theoptimization/planning system 130, etc.) can generate a set of potentialflight plans for the fleet of aircraft over the flight planning period.Each potential flight plan can include various types of information suchas, as examples: the identity of the aircraft, the identity of the pilot(if needed), the origin location, the destination location, a roughindication of flight path, an estimated time of departure, an estimatedtime of arrival, and/or various other information regarding thepotential operation of an aircraft.

The computing system (e.g., the optimization/planning system 130, etc.)can generate the set of potential flight plans that maximizesatisfaction of a combination of one or more objectives while alsoadhering to all of the constraints (i.e., not violating any of theconstraints). As examples, the objectives considered by the computingsystem (e.g., the optimization/planning system 130, etc.) can includemaximizing the number of potential flights generated, maximizing theratio of potential flight plans that become engaged, maximizing thenumber of engaged flights that operate at maximum passenger capacity,maximizing the number of passengers to whom a flight is delivered,maximizing the number of passengers that arrive at their destination ontime, minimizing the cumulative time period that passengers are delayedpast their desired arrival time, and/or various other objectives. Insome implementations, the computing system (e.g., theoptimization/planning system 130, etc.) can use a fleet-level objectivefunction that balances (e.g., using a set of weights) some or all of thedifferent objectives described above. The set of weights can be manuallytuned and/or automatically tuned (e.g., learned).

The computing system (e.g., the optimization/planning system 130, etc.)can perform one or more of various different algorithms to generate theset of potential flight plans. For example, in some implementations, thecomputing system (e.g., the optimization/planning system 130, etc.) caniteratively analyze the fleet of aircraft on an aircraft-by-aircraftbasis, generating an optimal set of potential flight plans for eachaircraft in turn. For example, the optimal set of potential flight plansfor an aircraft can be generated by optimizing, for such aircraft, anaircraft-level objective function that balances (e.g., using a set ofweights) a combination of any of the objectives described above. The setof weights in the aircraft-level objective function can be manuallytuned and/or automatically tuned (e.g., learned). In someimplementations, the aircraft-level objective function and thefleet-level objective function can be the same function and have thesame weights. In some implementations, the fleet-level objectivefunction can equal a sum of the aircraft-level objective function asrespectively applied to all aircraft in the fleet.

For example, FIG. 5 depicts a flow chart diagram of an example method500 to generate a set of potential flight plans on anaircraft-by-aircraft basis according to example embodiments of thepresent disclosure. One or more portion(s) of the method 500 can beimplemented by a computing system that includes one or more computingdevices such as, for example, the computing systems described withreference to the other figures (e.g., flight planning system, computingsystem 100, cloud services system 102, world state system(s) 126,forecasting system 128, optimization/planning system 130, matching andfulfillment system 132, etc.). Each respective portion of the method 500can be performed by any (or any combination) of one or more computingdevices. Moreover, one or more portion(s) of the method 500 can beimplemented as an algorithm on the hardware components of the device(s)described herein (e.g., as in FIGS. 1-3, etc.), for example, to generatea set of flight plans on an aircraft-by-aircraft basis. FIG. 5 depictselements performed in a particular order for purposes of illustrationand discussion. Those of ordinary skill in the art, using thedisclosures provided herein, will understand that the elements of any ofthe methods discussed herein can be adapted, rearranged, expanded,omitted, combined, and/or modified in various ways without deviatingfrom the scope of the present disclosure. FIG. 5 is described withreference to elements/terms described with respect to other systems andfigures for exemplary illustrated purposes and is not meant to belimiting. One or more portions of the method 500 can be performedadditionally, or alternatively, by other systems.

At (502), the method 500 can include receiving the data descriptive ofthe fleet of aircraft and the one or more flight planning constraintsfor the flight period. For example, a computing system (e.g., flightplanning system, computing system 100, cloud services system 102, worldstate system(s) 126, forecasting system 128, optimization/planningsystem 130, matching and fulfillment system 132, etc.) can receive thedata descriptive of the fleet of aircraft and the one or more flightplanning constraints for the flight period in the manner describedherein (e.g., with reference to FIG. 4).

At (504), the method 500 can include considering a next aircraft in thefleet. For example, the computing system (e.g., theoptimization/planning system 130, etc.) can consider the next aircraftin the fleet. At (506), the method 500 can include generating a set ofpotential flight plans for the currently considered aircraft for theentire planning period. For example, the computing system (e.g., theoptimization/planning system 130, etc.) can generate a set of potentialflight plans for the currently considered aircraft for the entireplanning period. In particular, the set of potential flight plans can beflight plans that optimize an aircraft-level objective function.

For example, the computing system (e.g., the optimization/planningsystem 130, etc.) can generate a set of flight plans for a particularaircraft by starting at the beginning of the flight planning period andsequentially generating/adding flight plans for the aircraft until theend of the flight planning period is reached. As one example, at eachinstance in which the computing system (e.g., the optimization/planningsystem 130, etc.) attempts to generate a new flight plan for anaircraft, the computing system (e.g., the optimization/planning system130, etc.) can generate a plurality of candidate flight plans, scoreeach candidate flight plan according to the aircraft-level objectivefunction, and then select and add the highest scoring flight plan forthe aircraft.

As described herein, the aircraft-level objective function can balanceany number of different objectives, including, as examples, variousobjectives which are a function of demand for the generated flight plan(e.g., total number of passengers serviced, total number of engagedflights, total number of flights that have all available seats filled,etc.). To evaluate such an objective function that includes objectivesthat are a function of demand, the system (e.g., theoptimization/planning system 130, etc.) can query a demand model (e.g.,of the world state/forecasting system(s) 126, 128, etc.) to obtainforecasted demand and/or supply information that can be used to evaluatesuch objectives. In another example, the aircraft-level objectivefunction may focus (e.g., include as the sole objective) on maximizingthe total number of flight plans generated.

In some implementations, the objective function can evaluate a set offlight plans based, at least in part, on an ultimate journey for aplurality of potential passengers. For example, an objective can includea function to reduce the total travel time for the plurality ofpassengers. By way of example, the demand data (e.g., from the worldstate/forecasting system(s) 126, 128, etc.) can include an anticipateddemand (e.g., forecasted demand data determined by the forecastingsystem 128, etc.) for transportation services. At times, thetransportation services can include multi-modal transportation services.In such a case, an aerial flight leg such as one of the set of potentialflight plans can include one of multiple destinations for a passengerbefore the passenger reaches a final destination. The objective functioncan evaluate the set of flight plans based, at least in part, on thetotal estimated travel time for the plurality of passengers. Forexample, the computing system (e.g., optimization/planning system 130,etc.) can identify infrastructural, traffic, weather, and/or otherfactors that can have an impact on a subsequent ground transportationleg and optimize the set of potential flight plans to lower the totalestimated travel time for users of the ride sharing network.

By way of example, the computing system (e.g., optimization/planningsystem 130, etc.) can take ground-based transportation information(e.g., provided by world state, forecasting system(s) 126, 128, serviceprovider computing device(s) 150, 160, 170, infrastructure andoperations computing device(s) 190, etc.) into account when generatingthe set of flight plans. For example, forecasted last mile supplyavailability can be used as an input to the aircraft-level objectivefunction to reward (e.g., increase objective score for) flights thatdeliver passengers to destinations which have a robust supply ofground-based transportation services and to penalize (e.g., decreaseobjective score for) flights that deliver passengers to destinationswhich do not have a robust supply of ground-based transportationservices. Similarly, forecasted first mile supply availability can beused as an input to the aircraft-level objective function to reward(e.g., increase objective score for) flights that collect passengersfrom departure nodes which have a robust supply of ground-basedtransportation services and to penalize (e.g., decrease objective scorefor) flights that collect passengers from departure nodes which do nothave a robust supply of ground-based transportation services. In otherimplementations, such information regarding supply of transportationservices according to other modalities can be assumed to be included inthe forecasted demand data.

The computing system (e.g., optimization/planning system 130, etc.) canevaluate the set of flight plans based, at least in part, on how closethe set of potential flight plans can transport potential passengers(e.g., expected based on forecasted demand data, etc.) to an expectedultimate destination. For example, the forecasted demand data caninclude an expected ultimate destination for each of a plurality ofanticipated passengers. The computing system (e.g.,optimization/planning system 130, etc.) can generate the set ofpotential flight plans in order to facilitate the transport of theplurality of anticipated passengers to a location (e.g., a transportnode) closest to the expected ultimate destination.

In some implementations, the closest location (e.g., transport node) tothe expected ultimate destination may not be the most efficient and/ortimely route for transporting the plurality of anticipated passengers.For example, a subsequent ground transportation from the closestlocation can be affected by traffic and/or other roadway delays.Moreover, in some implementations, the flight plan to the closestlocation can be affected by weather or other airspace delays. In such acase, the computing system (e.g., optimization/planning system 130,etc.) can generate/evaluate the set of flight plans in order tofacilitate the transport of the plurality of anticipated passengers to alocation (e.g., a transport node) closest, temporally, to the expectedultimate destination such that factors affecting the subsequenttransportation to an ultimate destination are accounted for.

In some implementations, the computing system (e.g.,optimization/planning system 130, etc.) can include a greedy refuelingcomponent. The greedy refueling component can greedily assign anaircraft to participate in refueling and/or recharging whenever theaircraft is not in-flight (e.g., for more than a minimum amount of time)and refueling/recharging infrastructure (e.g., as indicated byinfrastructure and operations computing device(s) 190, etc.) isavailable at the aircraft's current location. The greedy refuelingcomponent can be applied after generation of the flight plans or can beused as part of the generation process itself. In such fashion, the useof refueling/recharging infrastructure can be optimized.

In some implementations, the computing system (e.g.,optimization/planning system 130, etc.) can perform or participate in aniterative learning process for weights of planning objectivefunction(s). For example, an initial set of weights for the objectivefunction(s) can be manually tuned. The computing system (e.g.,optimization/planning system 130, etc.) can operate with the initial setof weights to generate any number of flight plans for any number offlight planning periods. Outcomes associated with the generated flightplans can be observed and measured according to various metrics. The setof weights can be retuned (e.g., automatically and/or manually) based onthe observed outcomes. For example, various learning techniques such asgradient descent techniques can be applied to learn the set of weights.For example, gradient descent techniques can be applied to the weightsof fleet-level objective function or the aircraft-level objectivefunction. More generally, the computing system (e.g.,optimization/planning system 130, etc.) can collect any data thatdescribes the outcomes of certain manually-controlled settings, actions,weightings, decisions, and/or the like and can apply machine learningtechniques to such data to learn updated or optimized versions of suchsettings, actions, weightings, decisions, and/or the like.

In this manner, the computing system (e.g., optimization/planning system130, etc.) can automatically generate the set of potential flight plansfor a fleet of aircraft owned and/or operated by one or more differentaerial operators. In some implementations, each flight plan can beapproved by a respective aerial vehicle operator (e.g., via serviceprovider computing device(s) 150, 160, 170, etc.). For example, eachaerial vehicle operator can be associated with a preapproval rule set.The preapproval ruleset, for example, can outline one or more acceptableflight plans (e.g., from/to/between one or more approved locations,within an approved time period, etc.) for aerial vehicles under theoperational control of the respective aerial vehicle operator. In someimplementations, the set of potential flight plans can only includeapproved flight plans. In addition, or alternatively, the set ofpotential flight plans can include unapproved flight plans. In such acase, the unapproved flight plans can be provided (e.g., escalated) tothe respective aerial vehicle operator for manual approval. Thecomputing system (e.g., optimization/planning system 130, etc.) canmodify and/or regenerate any flight plan that is not manually and/orpreapproved by a respective aerial vehicle operator.

At (508), the method 500 can include determining whether additionalaircraft remain in the fleet. For example, the computing system (e.g.,the optimization/planning system 130, etc.) can determine whetheradditional aircraft remain in the fleet.

If additional aircraft remain, then method 500 can return to (504) andconsider the next aircraft in the fleet. However, if it is determined,at (508), that all aircraft in the fleet have been considered, thenmethod 500 can proceed to (510)

At (510), the method 500 can include determining whether an additionaloptimization iteration should be performed. For example, the computingsystem (e.g., the optimization/planning system 130, etc.) can determinewhether an additional optimization iteration should be performed. Anynumber of iterations can be performed to allow for the flight plansgenerated for different aircraft to balance and move around each other.For example, the aircraft can be considered in a different sequence ateach iteration. Iterations can be performed until one or more stoppingcriteria are met. Stopping criteria can include a loop counter meeting athreshold, iteration-over-iteration change in objective functionscore(s) falling below a threshold, raw objective function score(s)exceeding a threshold, and/or other criteria. However, in someinstances, only a single iteration is performed.

If it is determined, at (510), that an additional iteration should beperformed, then method 500 can return to block (504) and perform anotheriteration of plan generation for some or all of the fleet. However, ifit is determined, at (510), that an additional iteration should not beperformed, then method 500 can proceed to (512).

At (512), the method 500 can include providing the set of potentialflights as an output. For example, the computing system (e.g., theoptimization/planning system 130, etc.) can provide the set of potentialflights as an output. As an example, the set of potential flights can beoutput to a matching and fulfillment system (e.g., matching andfulfillment system 132 of FIG. 1) configured to add passengers to one ormore of the set of potential flights to generate one or more engagedflights.

As another example, FIG. 6 depicts a flow chart diagram of an examplemethod 600 to generate a set of potential flight plans based on prioritytiers according to example embodiments of the present disclosure. One ormore portion(s) of the method 600 can be implemented by a computingsystem that includes one or more computing devices such as, for example,the computing systems described with reference to the other figures(e.g., flight planning system, computing system 100, cloud servicessystem 102, world state system(s) 126, forecasting system 128,optimization/planning system 130, matching and fulfillment system 132,etc.). Each respective portion of the method 600 can be performed by any(or any combination) of one or more computing devices. Moreover, one ormore portion(s) of the method 600 can be implemented as an algorithm onthe hardware components of the device(s) described herein (e.g., as inFIGS. 1-3, etc.), for example, to generate a set of flight plans basedon priority tiers. FIG. 6 depicts elements performed in a particularorder for purposes of illustration and discussion. Those of ordinaryskill in the art, using the disclosures provided herein, will understandthat the elements of any of the methods discussed herein can be adapted,rearranged, expanded, omitted, combined, and/or modified in various wayswithout deviating from the scope of the present disclosure. FIG. 6 isdescribed with reference to elements/terms described with respect toother systems and figures for exemplary illustrated purposes and is notmeant to be limiting. One or more portions of the method 600 can beperformed additionally, or alternatively, by other systems.

At (602), the method 600 can include receiving the data descriptive ofthe fleet of aircraft and the one or more flight planning constraintsfor the flight period. For example, a computing system (e.g., flightplanning system, computing system 100, cloud services system 102, worldstate system(s) 126, forecasting system 128, optimization/planningsystem 130, matching and fulfillment system 132, etc.) can receive thedata descriptive of the fleet of aircraft and the one or more flightplanning constraints for the flight period in the manner describedherein (e.g., with reference to FIG. 4).

At (602), the method 600 can include sorting time periods within theflight planning period into two or more priority tiers. For example, thecomputing system (e.g., optimization/planning computing system 130,etc.) can sort the plurality of time periods within the flight planningperiod into two or more priority tiers.

At (606), the method 600 can include considering the next priority tierof time periods. For example, the computing system (e.g.,optimization/planning computing system 130, etc.) can consider the nextpriority tier of time periods. For instance, at the first instance of(606), the computing system (e.g., optimization/planning computingsystem 130, etc.) can consider the highest priority tier of timeperiods. By way of example, in the multi-tiered approach, the computingsystem (e.g., optimization/planning computing system 130, etc.) caniteratively consider (e.g., by generating flight plans, etc.) the timeperiods in each priority tier, starting with the highest priority tierfirst. Thus, after considering the time periods of the highest prioritytier, the computing system (e.g., optimization/planning computing system130, etc.) can then consider the time periods of the next-highestpriority tier.

At (608), the method 600 can include generating a set of potentialflight plans for the fleet of aircraft for each time period in thecurrently considered priority tier. For example, the computing system(e.g., optimization/planning computing system 130, etc.) can generatethe set of potential flight plans for the fleet of aircraft for eachtime period in the currently considered priority tier. For example,method 500 of FIG. 5 can be performed for each time period in thecurrently considered priority tier. Each tier of time periods can usethe generated flight plans for the previous tier(s) as constraints forthe scheduling process. For example, if the generated flight plans forthe time period of 4 pm to 7 pm require that a given aircraft start at aparticular transportation node and with a particular fuel/charge level,then, when generating flight plans for the time period of 1 pm to 4 pm,the flight planning system can treat as constraints the fact that theaircraft needs to end the 1-4 pm time period at the particulartransportation node and with the particular fuel/charge level. In suchfashion, flight plans can be optimized specifically for the highestpriority time periods in which, for example, the largest number ofpassengers need to be serviced.

At (610), the method 600 can include determining whether additionalpriority tiers remain. For example, the computing system (e.g.,optimization/planning system 130, etc.) can determine whether additionalpriority tiers remain. If additional priority tiers remain, then method600 can return to 606 and consider the next priority tier of timeperiods. However, if it is determined, at (610), that additionalpriority tiers do not remain, then method 600 can proceed to 612. At(612), the method 600 can include providing the set of potential flightplans for the entire flight planning period as an output. For example,the computing system (e.g., optimization/planning system 130, etc.) canprovide the set of potential flight plans for the entire flight planningperiod as an output. As an example, the set of potential flight planscan be output to a matching and fulfillment system (e.g., matching andfulfillment system 132 of FIG. 1) configured to add passengers to one ormore of the set of potential flights to generate one or more engagedflights.

Turning back to FIG. 4, the method 400, to generate and use a set offlight plans within a ride sharing network, can include generating oneor more engaged flights based on a set of potential flight plans andpassenger demand.

At (406), the method 400 can include exposing the set of potentialflight plans (e.g., determine according to methods 500, 600, etc.) to aride sharing network and receiving passenger additions to one or moreflight plans, thereby transitioning potential flight plans to engagedflight plans. For example, the computing system (e.g.,optimization/planning computing system 130, matching and fulfillmentsystem 132, etc.) can expose the set of potential flight plans to theride sharing network and receive the passenger additions to the one ormore flight plans, thereby transitioning potential flight plans toengaged flight plans.

By way of example, after the computing system (e.g.,optimization/planning computing system 130, matching and fulfillmentsystem 132, etc.) generates the set of potential flight plans for thefleet and flight planning period, the set of potential flight plans canbe exposed to the ride sharing network. In particular, a matching system(e.g., matching system(s) 134 of FIG. 1, etc.) of the ride sharingnetwork can match one or more passengers of the network with aparticular flight plan according to a matching process, thereby causingthe flight plan to be engaged for operation. In some implementations,the matching system (e.g., matching system(s) 134 of FIG. 1, etc.) canperform passenger pooling in which multiple requests for service arecollected from users over a period and the matching servicescollectively analyzes the requests to identify opportunities to poolpassengers together. Although aspects of the present disclosure focus onproviding aerial transportation services to human passengers, thesystems and methods of the present disclosure are equally applicable todetermining flight plans for aerial transportation services to non-humanpayloads, such as packages, prepared foods, pet transportation, and/orthe like.

In some implementations, the set of potential flight plans can beexposed to the ride sharing network with minimal details. For example,the potential flight plans can be described by data indicative of anumber of passengers and/or trips that can be serviced during a block oftime at, between, and/or to a plurality of locations. The passengers ofthe ride sharing network can book a trip by booking an aerial transportover a block of time. The booked block of time can be utilized by theride sharing network to match a passenger to a flight from the potentialset of flights.

By way of example, passengers of the ride sharing network can booktravel (e.g., generally, flight specific, etc.) for a block of time. Theblock of time can be descriptive of a time slot container and can beused as an input for generating an engaged flight plan. The computingsystem (e.g., matching and fulfillment system 132, etc.) can receive aplurality of requests, group the plurality of requests within one ormore time slot containers, and wrap a flight (e.g., from the set ofpotential flights) around the time slot container. The passengers can begiven a price estimate, but not be charged until after the performanceof the flight. Before the performance of the flight, the passenger canbe provided information such as an aircraft type assigned to perform theflight, a time slot associated with the flight, and/or any otherinformation associated with the engaged flight.

In this manner, an engaged flight plan can be generated by addingpassengers to a potential flight plan of the set of potential flightplans. Thus, an engaged flight plan can include a potential flight planwith one or more assigned passengers. For example, each engaged flightplan can be associated with one or more assigned passengers of thepassengers associated with the ride sharing network. In addition, theengaged flight plan can include a scheduled take-off time, a scheduledlanding time, and a buffer time period. The buffer time period can beindicative of a delay from the scheduled take-off time (e.g., anacceptable delay). The buffer time period can be determined based, atleast in part, on the input data, the one or more constraints (e.g.,weather, traffic, deviations, etc.), and/or the time period. Forexample, the engaged flight plan can form a user-centric flight schedulewith an adjustable take-off time based, at least in part, on theassigned passengers associated with the engaged flight plan. Thisadjustable, user-centric, take-off time can be represented by the buffertime period.

More particularly, the engaged flight plan can include a buffer timeperiod determined based on wholistic inputs centered around passengerconvenience. By way of example, the computing system (e.g., matching andfulfillment system 132, etc.) can generate an engaged flight plan based,at least in part, on one or more assigned passengers (e.g., added by theride sharing network). The computing system (e.g., matching andfulfillment system 132, etc.) can determine a buffer time period for theengaged flight plan based, at least in part, on the one or more assignedpassengers of the engaged flight plan.

For example, each of the one or more assigned passengers can beassociated with a multi-leg travel itinerary. The multi-leg travelitinerary can include, for example, multiple travel legs via one or moredifferent modalities. For instance, one leg of the multi-leg travelitinerary can include the engaged flight plan. Additional travel legscan include ground transportation to an origin location of the engagedflight plan, another ground transportation from the destination locationof the engaged flight plan, and/or one or more additional flight legspreceding and/or subsequent to the engaged flight plan.

In some implementations, the computing system (e.g., matching andfulfillment system 132, etc.) can determine the buffer time periodbased, at least in part, on the multi-leg travel itinerary for each ofthe one or more assigned passengers. For instance, the multi-leg travelitinerary can be associated with a total estimated travel time for anassigned passenger. In such a case, the buffer time period can bedetermined based, at least in part, on an aggregated delay time periodto the multi-leg travel itinerary of each of the one or more assignedpassengers as a result of delaying the engaged flight plan by one ormore different periods of time. The buffer time period, for example, canbe determined to ensure that the aggregated delay period is less than athreshold time (an hour, 30 minutes, 10 minutes, etc.). In some cases,the threshold time can include a time period less than the duration ofan engaged flight. As one example for illustration purposes, thecomputing system (e.g., matching and fulfillment system 132, etc.) canpredict a higher rate of traffic for ground transportation at a firsttime period after the engaged flight. In such a case, the computingsystem (e.g., matching and fulfillment system 132, etc.) can determine abuffer time period that is less than the first time period to preventthe affected passenger from being further delayed by foreseeabletraffic.

In addition, or alternatively, the buffer time period can be determinedbased, at least in part, on a number of the one or more assignedpassengers that will have a change to their multi-leg travel itineraryas a result of delaying the engaged flight plan by one or more differentperiods of time. For example, the buffer time period can be determinedto avoid causing one or more assigned passengers to miss a subsequentflight.

At (408), the method 400 can include monitoring the adherence of thefleet of aircraft and/or passengers to the engaged flight plans. Forexample, the computing system (e.g., matching and fulfillment system132, monitoring and mitigation system 136, etc.) can monitor adherenceof the fleet of aircraft and/or passengers to the engaged flight plans.

More particularly, the computing system (e.g., matching and fulfillmentsystem 132, monitoring and mitigation system 136, etc.) can continuouslymonitor the success/viability of each engaged flight plan. For example,the computing system (e.g., matching and fulfillment system 132,monitoring and mitigation system 136, etc.) can monitor real-time datasuch as aircraft location data (e.g., received from service providercomputing device(s) 150, 160, 170, etc.), aircraft sensor data (e.g.,from service provider computing device(s) 150, 160, 170, infrastructureand operations computing device(s) 190, etc.), passenger location data(e.g., with permission, received from a rider computing device(s) 140,service provider computing device(s) 150, 160, 170, etc.), weather data(e.g., from forecasting system 128, etc.), ground-based transportationdata (e.g., received from service provider computing device(s) 150, 160,170, etc.), and/or other forms of data to detect current or likelydeviations of the fleet of aircraft and/or passengers from engagedflight plans. In particular, the computing system (e.g., matching andfulfillment system 132, monitoring and mitigation system 136, etc.) canmonitor and evaluate the estimated times of arrival for some or allpassengers versus planned times of arrival, the estimated times ofarrival for some or all aircraft versus planned times of arrival, and/orother measures of success of flight plans.

In this manner, at (408), the computing system (e.g., matching andfulfillment system 132, monitoring and mitigation system 136, etc.) cancontinuously assess whether flights will successfully depart and/orarrive at their scheduled times, including tracking whether assignedpassengers will be able to successfully arrive at the departingtransportation node in sufficient time to physically progress throughthe transportation node and embark on the aircraft. In particular, insome implementations, the estimated arrival time can be for a passengerand can be based on a first leg of a multi-leg itinerary. For example, auser may use ground-based transportation (e.g., a ride shared car) toget to the transportation node and, as such, the computing system (e.g.,matching and fulfillment system 132, monitoring and mitigation system136, etc.) can track the user's progress along the ground-basedtransportation leg to assess whether the user will arrive in sufficienttime to avoid delaying their associated flight (e.g., which may be thesecond leg of the multi-leg itinerary).

The computing system (e.g., matching and fulfillment system 132,monitoring and mitigation system 136, etc.) can track the user location,for example, by receiving location data associated with one or more ofthe one or more passengers. For example, the computing system (e.g.,matching and fulfillment system 132, monitoring and mitigation system136, etc.) can interact (e.g., via one or more service providerdevice(s) 150, 160, 170, etc.) with the ride sharing network to receiveupdates to a passenger's location. As an example, the computing system(e.g., matching and fulfillment system 132, monitoring and mitigationsystem 136, etc.) can receive passenger location data associated with arespective passenger from a ground vehicle device assigned to therespective passenger for providing ground transportation to therespective passenger before a respective engaged flight plan for thepassenger. The location data can be received in real-time, periodically,during the course of travel from the passenger's origin location to thefirst transportation node. In addition, or alternatively, the locationdata can be received in response to a detected deviation from anoriginal estimated time of arrival. For example, the location data for apassenger can be indicative of a delayed or earlier estimated time ofarrival for a passenger. In some implementations, the location data caninclude traffic information, driver information, flight information,and/or any other information associated with a passenger'stransportation to a first transportation node of the engaged flight.

At (410), the method 400 can include detecting one or more deviations ofthe fleet of aircraft and/or passengers from the engaged flight plans.For example, the computing system (e.g., matching and fulfillment system132, monitoring and mitigation system 136, etc.) can detect the one ormore deviations of the fleet of aircraft and/or passengers from theengaged flight plans. The computing system (e.g., matching andfulfillment system 132, monitoring and mitigation system 136, etc.) canperform real-time mitigation and replanning when a particular flightplan becomes significantly delayed or cancelled/unfulfilled. Typically,the computing system (e.g., matching and fulfillment system 132,monitoring and mitigation system 136, etc.) can attempt to delayreplanning activities until it is believed with significant probabilitythat an engaged flight plan will not be able to be successfullycompleted.

At (412), the method 400 can include adjusting the flight plans toaccount for the deviation(s). For example, the computing system (e.g.,matching and fulfillment system 132, monitoring and mitigation system136, etc.) can adjust the flight plans to account for the deviation(s).For instance, the computing system (e.g., matching and fulfillmentsystem 132, monitoring and mitigation system 136, etc.) can performreal-time mitigation and replanning based, at least in part, on one ormore deviations of the one or more passengers. For example, thecomputing system (e.g., matching and fulfillment system 132, monitoringand mitigation system 136, etc.) can reoptimize the set of potentialflight plans and/or the one or more engaged flight plans based, at leastin part, on minimizing inconveniences to passengers of the ride sharingnetwork. By way of example, the computing system (e.g., matching andfulfillment system 132, monitoring and mitigation system 136, etc.) candetermine a mitigation action (e.g., delay a flight, reassign one ormore passengers to different engaged/potential flights, etc.) inresponse to one or more deviations of the fleet of aircraft and/orpassengers. For each available mitigation action, the computing system(e.g., matching and fulfillment system 132, monitoring and mitigationsystem 136, etc.) can determine a number of passengers that will beimpacted as a result of the mitigation action. By way of example, thecomputing system (e.g., matching and fulfillment system 132, monitoringand mitigation system 136, etc.) can determine a number of passengersthat will miss their arrive by times as a result of the mitigationaction, an aggregate time period that will be added to all thepassengers' transportation services as a result of the mitigationaction, and/or other metrics. In this manner, the computing system(e.g., matching and fulfillment system 132, monitoring and mitigationsystem 136, etc.) can adjust one or both of the engaged flight plansand/or the set of potential flight plans to account for the one or moredeviations with minimal negatively impacting the convenience ofpassengers engaged in an on demand transportation service.

In some implementations, the mitigation process can include delaying aflight based on the buffer time period of an engaged flight plan. Forexample, the buffer time period can be indicative of a time periodbefore and/or after a scheduled take-off that is allowable for theaircraft. For example, allowances can be made for accommodating a lateand/or early passenger based, at least in part, on an understanding ofthe user (e.g., location thereof) and other pooled users associated withthe engaged flight. For example, the buffer time period can bedetermined based on a group inconvenience factor and/or a trickle downimpact of a delay take-off time. The computing system (e.g., matchingand fulfillment system 132, monitoring and mitigation system 136, etc.)can determine that a deviation is due to a late assigned passenger foran engaged flight plan. The computing system (e.g., matching andfulfillment system 132, monitoring and mitigation system 136, etc.) candetermine an estimated time of arrival for the late assigned passengerand determine an adjustment for the engaged flight plan based, at leastin part, on the estimated time of arrival for the late assignedpassenger and the buffer time period. The computing system (e.g.,matching and fulfillment system 132, monitoring and mitigation system136, etc.) can apply the adjustment to the engaged flight plan.

By way of example, in response to determining that the estimated time ofarrival for the late assigned passenger is after the scheduled take-offtime by a time period greater than the buffer time period, the computingsystem (e.g., matching and fulfillment system 132, monitoring andmitigation system 136, etc.) can add the late assigned passenger to oneor more of the set of potential flight plans (and/or engaged flightplans with space for the late assigned passenger). In addition, oralternatively, in response to determining that the estimated time ofarrival for the late passenger is after the scheduled take-off time andwithin the buffer time period, the computing system (e.g., matching andfulfillment system 132, monitoring and mitigation system 136, etc.) candelay the scheduled take-off time for the engaged flight plan toaccommodate the late assigned passenger. In some implementations, thecomputing system (e.g., matching and fulfillment system 132, monitoringand mitigation system 136, etc.) can automatically transmitnotifications to one or more of: the late assigned passenger (e.g., viaa respective rider computing device 140), operations personnel (e.g.,via infrastructure and operations computing devices 190), and/oraircraft/ground vehicle operators (e.g., via service provider computingdevices 150, 160, 170).

In some implementations, the mitigation process can include manualinputs by human mitigation personnel. For example, the need to performmitigation can be automatically detected and, as a result, the computingsystem (e.g., matching and fulfillment system 132, monitoring andmitigation system 136, etc.) can provide an alert and a mitigation userinterface to a human mitigation personnel. For example, the mitigationuser interface can include a graphical user interface that showspotential alternative flight plans for and/or changes to a flight planthat is currently subject to delay/cancellation. As one example, thehuman personnel can interact with the interface to adjust variousparameters of one or more flight plans. For example, the human personnelcan alter flight departure times, alter buffer times associated withphysical passage of passengers through transportation nodes and/orvehicle embarkation/disembarkation, add or remove passengers fromflights, move passengers between flights, change pilots, and/or otheractions to manually alter the set of flight plans. In someimplementations, any downstream effects on the flight plans from amanual change can be automatically computed and propagated through theset of flight plans.

In some implementations, the user interface can provide warnings orother indications of how certain mitigation activities or potentialactions might affect other users of the system and/or violate certain ofthe initial input constraints. For example, if the mitigation personnelattempts to delay a not-yet-departed flight plan to wait for a delayeduser, the user interface can inform the mitigation personnel that suchaction would impact 3 other travelers. The warnings/indications providedin the user interface can provide impact information according tovarious metrics including, for each available choice/action, a number ofusers that will be impacted as a result of the choice/action, a numberof users that will miss their arrive by times as a result of thechoice/action, an aggregate time period that will be added to all theusers' transportation services as a result of the choice/action, and/orother metrics. Generally, preference can be given to mitigationstrategies that have minimal impacts on other passengers. In addition,certain constraints (e.g., final aircraft destination at the end of theflight planning period) can be manually violated while other constraints(e.g., safety constraints such as maximum weight in the aircraft) cannotbe manually violated.

In another example, human personnel can change one or more constraintsand then rerun the flight planning process. For example, if extremeweather renders a certain subset of the transportation nodes unusablefor a period of time, the human personnel can adjust the constraints tomark the subset of transportation nodes as unavailable for the period oftime and then rerun the flight planning process. Thus, certainadjustments can be made directly to individual flight plans while otheradjustments may require a system-wide readjustment or replanning.

In some implementations, the mitigation process can be automated (e.g.,with the ability for manual overrides). As an example, the computingsystem (e.g., matching and fulfillment system 132, monitoring andmitigation system 136, etc.) can continuously generate contingencyflight plans. For example, the contingency flight plans can be generatedusing a process as described above but taking into account potential oractual delays in certain flight plans. When it is detected thatmitigation interventions should be performed, the computing system(e.g., matching and fulfillment system 132, monitoring and mitigationsystem 136, etc.) can automatically select the best availablecontingency flight plans and push the selected flight plans out to eachaircraft and other system component. For example, automatic updates andalerts can be sent to passengers, aircraft providers, operationspersonnel, and/or other integrated systems. The contingency flight planscan be ranked based on various metrics including, for each potential setof updated flight plans, a number of passengers that will be impacted asa result of the choice/action, a number of passengers that will misstheir arrive by times as a result of the choice/action, an aggregatetime period that will be added to all the passengers' transportationservices as a result of the choice/action, and/or other metrics.Alternatively, or additionally, the objective function(s) can be used toscore the contingency flight plans and/or replan for some or all of thefleet of aircraft. Thus, in some instances, the dynamic contingencygeneration can be viewed as a continuous fleet-wide reoptimization offlight plans based on real-time conditions.

In some implementations, the mitigation process can be automated andimplemented within the bounds of preapproval criteria established by theone or more aircraft operators. For example, the computing system (e.g.,matching and fulfillment system 132, monitoring and mitigation system136, etc.) can detect one or more deviations of the fleet of aircraftand/or the one or more passengers from the engaged flight plans. Thecomputing system (e.g., matching and fulfillment system 132, monitoringand mitigation system 136, etc.) can determine an adjustment for atleast one of the engaged flight plans and/or the set of potential flightplans to accommodate for the one or more deviations. The computingsystem (e.g., matching and fulfillment system 132, monitoring andmitigation system 136, etc.) can compare the adjustment to preapprovalcriteria for an operator of the at least one of the engaged flight plansor the set of potential flight plans. In response to determining thatthe adjustment achieves the preapproval criteria, the computing system(e.g., matching and fulfillment system 132, monitoring and mitigationsystem 136, etc.) can automatically adjust the at least one of theengaged flight plan and/or the set of potential flight plans. Inresponse to determining that the adjustment does not achieve thepreapproval criteria, the computing system (e.g., matching andfulfillment system 132, monitoring and mitigation system 136, etc.) cantransmit data indicative of the adjustment to human mitigationpersonnel.

Additional Disclosure

The use of computer-based systems allows for a great variety of possibleconfigurations, combinations, and divisions of tasks and functionalitybetween and among components. Computer-implemented operations can beperformed on a single component or across multiple components.Computer-implemented tasks and/or operations can be performedsequentially or in parallel. Data and instructions can be stored in asingle memory device or across multiple memory devices.

While the present subject matter has been described in detail withrespect to various specific example embodiments thereof, each example isprovided by way of explanation, not limitation of the disclosure. Thoseskilled in the art, upon attaining an understanding of the foregoing,can readily produce alterations to, variations of, and equivalents tosuch embodiments. Accordingly, the subject disclosure does not precludeinclusion of such modifications, variations and/or additions to thepresent subject matter as would be readily apparent to one of ordinaryskill in the art. For instance, features illustrated or described aspart of one embodiment can be used with another embodiment to yield astill further embodiment. Thus, it is intended that the presentdisclosure cover such alterations, variations, and equivalents.

In particular, although FIGS. 4, 5, and 6 respectively depict stepsperformed in a particular order for purposes of illustration anddiscussion, the methods of the present disclosure are not limited to theparticularly illustrated order or arrangement. The various steps of themethods 400, 500, and 600 can be omitted, rearranged, combined, and/oradapted in various ways without deviating from the scope of the presentdisclosure.

What is claimed is:
 1. A computing system configured to generate flightplans for a ride sharing network, the computing system comprising: oneor more processors; and one or more non-transitory computer-readablemedia that collectively store instructions that, when executed by theone or more processors, cause the computing system to performoperations, the operations comprising: receiving input data descriptiveof a fleet of aircraft, one or more constraints, and a flight planningperiod; generating a set of potential flight plans for the fleet ofaircraft and the flight planning period based at least in part on theinput data; exposing the set of potential flight plans to the ridesharing network; and receiving, from the ride sharing network, one ormore additions of one or more passengers to one or more of the set ofpotential flight plans to generate one or more engaged flight plans. 2.The computing system of claim 1, wherein the input data comprisesforecasted demand data that describes a forecasted demand for respectiveflights at different respective times and locations, wherein theforecasted demand data is based, at least in part, on the ride sharingnetwork.
 3. The computing system of claim 1, wherein the input datacomprises forecasted supply data descriptive of a forecasted supply ofground-based transportation providers at different respective times andtransportation locations, wherein the forecasted supply data is based,at least in part, on the ride sharing network.
 4. The computing systemof claim 1, wherein the operations further comprise: monitoringadherence of the fleet of aircraft to the engaged flight plans;detecting one or more deviations of the fleet of aircraft or the one ormore passengers from the engaged flight plans; and adjusting one or bothof the engaged flight plans or the set of potential flight plans toaccount for the one or more deviations.
 5. The computing system of claim4, wherein monitoring adherence of the fleet of aircraft to the engagedflight plans comprises tracking one or both of: location data associatedwith one or more aircraft of the fleet of aircraft; or location dataassociated with one or more of the one or more passengers.
 6. Thecomputing system of claim 5, wherein tracking the location dataassociated with the one or more of the one or more passengers comprises:receiving passenger location data associated with a respective passengerfrom a ground vehicle device assigned to the respective passenger forproviding ground transportation to the respective passenger before arespective engaged flight plan for the passenger.
 7. The computingsystem of claim 4, wherein each engaged flight plan is associated withone or more assigned passengers of the one or more passengers, ascheduled take-off time, a scheduled landing time, and a buffer timeperiod, wherein the buffer time period is indicative of a delay from thescheduled take-off time.
 8. The computing system claim 7, whereingenerating an engaged flight plan of the one or more engaged flightplans comprises: determining the buffer time period for the engagedflight plan based, at least in part, on the one or more assignedpassengers of the engaged flight plan.
 9. The computing system claim 8,wherein each of the one or more assigned passengers are associated witha multi-leg travel itinerary, wherein one leg of the multi-leg travelitinerary is the engaged flight plan, and wherein the buffer time periodis determined based, at least in part, on the multi-leg travel itineraryfor each of the one or more assigned passengers.
 10. The computingsystem claim 9, wherein the multi-leg travel itinerary is associatedwith a total estimated travel time for an assigned passenger, andwherein the buffer time period is determined based, at least in part, onan aggregated time period to the multi-leg travel itinerary of each ofthe one or more assigned passengers as a result of delaying the engagedflight plan by one or more different periods of time.
 11. The computingsystem claim 9, wherein the buffer time period is determined based, atleast in part, on a number of the one or more assigned passengers thatwill have a change to their multi-leg travel itinerary as a result ofdelaying the engaged flight plan by one or more different periods oftime.
 12. The computing system claim 7, wherein adjusting one or both ofthe one or more engaged flight plans or the set of potential flightplans to account for the one or more deviations comprises: determiningthat a deviation is due to a late assigned passenger for an engagedflight plan; determining an estimated time of arrival for the lateassigned passenger; determining an adjustment for the engaged flightplan based, at least in part, on the estimated time of arrival for thelate assigned passenger; and applying the adjustment to the engagedflight plan.
 13. The computing system claim 12, wherein determining theadjustment for the engaged flight plan comprises: in response todetermining that the estimated time of arrival for the late assignedpassenger is after the scheduled take-off time by a time period greaterthan the buffer time period, adding the late assigned passenger to oneor more of the set of potential flight plans; in response to determiningthat the estimated time of arrival for the late passenger is after thescheduled take-off time and within the buffer time period, delaying thescheduled take-off time for the engaged flight plan to accommodate thelate assigned passenger; and automatically transmitting notifications toone or more of: the late assigned passenger, operations personnel, oraircraft operators.
 14. A computer-implemented method to generate flightplans for a ride sharing network, the method comprising: receiving, by acomputing system comprising one or more computing devices, input datadescriptive of a fleet of aircraft, one or more constraints, and aflight planning period; generating, by the computing system, a set ofpotential flight plans for the fleet of aircraft and the flight planningperiod based at least in part on the input data; exposing, by thecomputing system, the set of potential flight plans to the ride sharingnetwork; and receiving, by the computing system from the ride sharingnetwork, one or more additions of one or more passengers to one or moreof the set of potential flight plans to generate one or more engagedflight plans.
 15. The computer-implemented method of claim 14, whereingenerating the set of potential flight plans for the fleet of aircraftcomprises generating the set of potential flight plans for the fleet ofaircraft that optimizes an objective function.
 16. Thecomputer-implemented method of claim 15, wherein the objective functionevaluates the set of potential flight plans according to one or moremetrics comprising: a quantity of the set of potential flight plans; aratio of the set of potential flight plans that are expected to beengaged by passengers; a number of the set of potential flight plansthat are expected to operate at maximum passenger capacity; a number ofpassengers expected to be serviced by the set of potential flight plans;a number or percentage of passengers of the set of potential flightplans expected to arrive at a respective destination in advance of adesired arrival time; or an estimated time period that passengers of theset of potential flight plans are expected to be delayed past thedesired arrival time.
 17. The computer-implemented method of claim 15,wherein the method further comprises: learning updated values for a setof weights of the objective function based at least in part on observedoutcome data.
 18. The computer-implemented method of claim 14, whereingenerating the set of potential flight plans for the fleet of aircraftcomprises: sorting a plurality of time periods included within theflight planning period into two or more priority tiers; and iterativelygenerating a set of potential flight plans for the time periods in eachpriority tier, starting from a highest priority tier; wherein potentialflight plans generated for a higher priority tier are used to generateconstraints to be met by potential flight plans generated for a lowerpriority tier.
 19. The computer-implemented method of claim 14, furthercomprising: detecting, by the computing system, one or more deviationsof the fleet of aircraft or the one or more passengers from the engagedflight plans; determining, by the computing system, an adjustment for atleast one of the engaged flight plans or the set of potential flightplans to accommodate for the one or more deviations; comparing, by thecomputing system, the adjustment to preapproval criteria for an operatorof the at least one of the engaged flight plans or the set of potentialflight plans; in response to determining that the adjustment achievesthe preapproval criteria, adjusting, by the computing system, the atleast one of the engaged flight plan or the set of potential flightplans; and in response to determining that the adjustment does notachieve the preapproval criteria, transmitting, by the computing system,data indicative of the adjustment to human mitigation personnel.
 20. Oneor more non-transitory computer-readable media comprising instructionsthat when executed by one or more computing devices cause the one ormore computing devices to perform operations comprising: receiving inputdata descriptive of a fleet of aircraft, one or more constraints, and aflight planning period; generating a set of potential flight plans forthe fleet of aircraft and the flight planning period based at least inpart on the input data; exposing the set of potential flight plans to aride sharing network; and receiving, from the ride sharing network, oneor more additions of one or more passengers to one or more of the set ofpotential flight plans to generate one or more engaged flight plans.